Industry Growth Insights published a new data on “Machine Learning in Communication Market”. The research report is titled “Machine Learning in Communication Market research by Types (Cloud-Based, On-Premise), By Applications (Network Optimization, Predictive Maintenance, Virtual Assistants, Robotic Process Automation (RPA)), By Players/Companies Amazon, IBM, Microsoft, Google, Nextiva, Nexmo, Twilio, Dialpad, Cisco, RingCentral”.
Scope Of The Report
Report Attributes
Report Details
Report Title
Machine Learning in Communication Market Research Report
By Type
Cloud-Based, On-Premise
By Application
Network Optimization, Predictive Maintenance, Virtual Assistants, Robotic Process Automation (RPA)
By Companies
Amazon, IBM, Microsoft, Google, Nextiva, Nexmo, Twilio, Dialpad, Cisco, RingCentral
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
139
Number of Tables & Figures
98
Customization Available
Yes, the report can be customized as per your need.
Global Machine Learning in Communication Market Report Segments:
The global Machine Learning in Communication market is segmented on the basis of:
Types
Cloud-Based, On-Premise
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Network Optimization, Predictive Maintenance, Virtual Assistants, Robotic Process Automation (RPA)
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- Amazon
- IBM
- Microsoft
- Nextiva
- Nexmo
- Twilio
- Dialpad
- Cisco
- RingCentral
Highlights of The Machine Learning in Communication Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Cloud-Based
- On-Premise
- By Application:
- Network Optimization
- Predictive Maintenance
- Virtual Assistants
- Robotic Process Automation (RPA)
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Machine Learning in Communication Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
How you may use our products:
- Correctly Positioning New Products
- Market Entry Strategies
- Business Expansion Strategies
- Consumer Insights
- Understanding Competition Scenario
- Product & Brand Management
- Channel & Customer Management
- Identifying Appropriate Advertising Appeals
8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Machine learning is a subset of artificial intelligence that uses algorithms to learn from data. It can be used to improve the accuracy and speed of predictions made by computers about future events, trends, or customer behavior.
Some of the major companies in the machine learning in communication market are Amazon, IBM, Microsoft, Google, Nextiva, Nexmo, Twilio, Dialpad, Cisco, RingCentral.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Machine Learning in Communication Market Overview 4.1 Introduction 4.1.1 Market Taxonomy 4.1.2 Market Definition 4.1.3 Macro-Economic Factors Impacting the Market Growth 4.2 Machine Learning in Communication Market Dynamics 4.2.1 Market Drivers 4.2.2 Market Restraints 4.2.3 Market Opportunity 4.3 Machine Learning in Communication Market - Supply Chain Analysis 4.3.1 List of Key Suppliers 4.3.2 List of Key Distributors 4.3.3 List of Key Consumers 4.4 Key Forces Shaping the Machine Learning in Communication Market 4.4.1 Bargaining Power of Suppliers 4.4.2 Bargaining Power of Buyers 4.4.3 Threat of Substitution 4.4.4 Threat of New Entrants 4.4.5 Competitive Rivalry 4.5 Global Machine Learning in Communication Market Size & Forecast, 2018-2028 4.5.1 Machine Learning in Communication Market Size and Y-o-Y Growth 4.5.2 Machine Learning in Communication Market Absolute $ Opportunity
Chapter 5 Global Machine Learning in Communication Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Machine Learning in Communication Market Size Forecast by Type
5.2.1 Cloud-Based
5.2.2 On-Premise
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Machine Learning in Communication Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Machine Learning in Communication Market Size Forecast by Applications
6.2.1 Network Optimization
6.2.2 Predictive Maintenance
6.2.3 Virtual Assistants
6.2.4 Robotic Process Automation (RPA)
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Machine Learning in Communication Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Machine Learning in Communication Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Machine Learning in Communication Analysis and Forecast
9.1 Introduction
9.2 North America Machine Learning in Communication Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Machine Learning in Communication Market Size Forecast by Type
9.6.1 Cloud-Based
9.6.2 On-Premise
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Machine Learning in Communication Market Size Forecast by Applications
9.10.1 Network Optimization
9.10.2 Predictive Maintenance
9.10.3 Virtual Assistants
9.10.4 Robotic Process Automation (RPA)
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Machine Learning in Communication Analysis and Forecast
10.1 Introduction
10.2 Europe Machine Learning in Communication Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Machine Learning in Communication Market Size Forecast by Type
10.6.1 Cloud-Based
10.6.2 On-Premise
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Machine Learning in Communication Market Size Forecast by Applications
10.10.1 Network Optimization
10.10.2 Predictive Maintenance
10.10.3 Virtual Assistants
10.10.4 Robotic Process Automation (RPA)
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Machine Learning in Communication Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Machine Learning in Communication Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Machine Learning in Communication Market Size Forecast by Type
11.6.1 Cloud-Based
11.6.2 On-Premise
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Machine Learning in Communication Market Size Forecast by Applications
11.10.1 Network Optimization
11.10.2 Predictive Maintenance
11.10.3 Virtual Assistants
11.10.4 Robotic Process Automation (RPA)
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Machine Learning in Communication Analysis and Forecast
12.1 Introduction
12.2 Latin America Machine Learning in Communication Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Machine Learning in Communication Market Size Forecast by Type
12.6.1 Cloud-Based
12.6.2 On-Premise
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Machine Learning in Communication Market Size Forecast by Applications
12.10.1 Network Optimization
12.10.2 Predictive Maintenance
12.10.3 Virtual Assistants
12.10.4 Robotic Process Automation (RPA)
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Machine Learning in Communication Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Machine Learning in Communication Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Machine Learning in Communication Market Size Forecast by Type
13.6.1 Cloud-Based
13.6.2 On-Premise
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Machine Learning in Communication Market Size Forecast by Applications
13.10.1 Network Optimization
13.10.2 Predictive Maintenance
13.10.3 Virtual Assistants
13.10.4 Robotic Process Automation (RPA)
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Machine Learning in Communication Market: Competitive Dashboard
14.2 Global Machine Learning in Communication Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 Amazon
14.3.2 IBM
14.3.3 Microsoft
14.3.4 Google
14.3.5 Nextiva
14.3.6 Nexmo
14.3.7 Twilio
14.3.8 Dialpad
14.3.9 Cisco
14.3.10 RingCentral